Analytics

Data, and the need to analyze it has been existing since the early days of application building. Conventional datastores (RDBMS, Files) offered the ability to store, extract, and analyze well-structured data in a post-facto manner. The need for analyzing data at the very moment it was processed for business functionality, was never critical. As application ecosystems became complex, distributed, and multi-tenanted in nature, the data came from disparate sources, started losing its ‘structured’ nature and streamed in enormous volumes. Big data/Hadoop systems started replacing conventional data stores.

In today’s world, businesses have to rely heavily on the need to not just process, but also analyze data in real-time. Second, the enormity of data, low-latency and high-throughput requirements have rendered conventional data processing architectures and databases ineffective. Moreover, Actionable Insights – both Operational and Business, are now sought from combinationof: historical, real-time, and predictive/machine learning based analytics.

Additionally, applications need to be enabled for processing enormous data on clusters in a hybrid setup of on-premise and cloud. When enterprises think of hosting an array of applications on cloud, the data persistence often needs a different approach. Conventional data warehouses prove to be both – non-scalable and super expensive. Data Lakes are needed in such scenarios.

With over a decade of expertise in applications space, GS Lab has enabled enterprises to immensely benefit from their own data by building actionable insights and visualizations that have allowed the enterprises to seek Operational and Business insights – both in real time and post facto. We have done so by creating setups that use the modern, state of the art techniques built on streaming and big data analytics, and data lakes, as well as leverage their existing investments – conventional datastores, warehouses and applications.

Business Intelligence (BI)

Businesses often have dependencies on external factors - market, competition, customers, and suppliers. Having a holistic view of the data - internal and external - provides competitive advantage by identifying inefficiencies, plug leakages and bringing in innovation.

Analytics

Like any modern applications, those that leverage Cloud (AWS or Azure) infrastructure and services also have to deal with vast amount of data. Whether applications are built using Cloud services or not, AWS and Azure offer a variety of analytical tools, which are powerful, scalable and simple to use.

Historical Insights

Historical analytics is the analysis of data from the past - seek correlations, trends, patterns that unearth insights into business performance and operational efficiencies. Whether data resides in files, databases, or Hadoop systems - analyzing and correlating the data is vital to your business.

Machine Learning

The ability to generate more meaningful intelligence needs modification as data becomes more and more insightful. Pattern recognition gains importance as data becomes more varied in content. The Analyzing mechanisms need to teach themselves to be smarter with time. The ability to automatically apply these learnings to Big Data becomes a taller need.

Real Time Insights

Applications benefit tremendously from Operational and Business Insights in Real Time. This is often achieved by creating complex setups that combine streaming frameworks, NoSQL data stores, and Hadoop ecosystem to process high throughput data and provide actionable intelligence at sub-second levels. Weaving together such high data processing frameworks often becomes a challenge.

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